Before I studied student success, I earned a PhD in evolutionary biology.
I often find myself thinking about how the two topics relate, and whether we can apply evolutionary thinking to student success challenges.
Lineages of species divide over time and give rise to descendants that resemble their forebearers but are also distinct in unique ways. To my (admittedly American) eyes, the major schools of thought on student success have evolved in the same way.
As these “lineages” have developed, so have the metrics we use to find opportunities and track progress. As you’ll see below, these metrics have become very complex over the span of just a few years. We are also in a moment of transition as we emerge from the pandemic and struggle to understand its ripple effects.
Not coincidentally, EAB has seen a recent uptick in requests for help building dashboards and tracking large amounts of student success data. Institutions around the world seem convinced (or have been convinced) that tracking the right metric (or two, or twenty) will solve everything.
As we think about where student success is headed in the next decade, it’s useful to look back at where we’ve been. I’ll review how our understanding of student success (and how to measure it) has evolved by exploring four major “lineages” of student success best practice.
Lineage A: Student engagement
The first of these modern lineages emerged during the 1970s from the work of early student success researchers such as Vincent Tinto, who pioneered and popularised the idea of student engagement. In this model, attrition results from a lack of attachment and connection to the institution and its environs, meaning that students who feel engaged and part of the community are more likely to return.
Student engagement efforts coalesced under the umbrella of first-year experience during the 1990s and early 2000s. Advocates reasoned that engagement efforts should be focused on incoming students as they form their first impressions and set out on their course of study. They also reasoned that they could reduce attrition by catching and helping students struggling with the difficult transition to university. Doing this correctly would result in more students returning for a second year; thus, first-year retention became the metric widely used to track student success. (And for which, I’ll note, the UK remains the envy of almost every other country.)
Lineage B: Degree planning
Even as first-year experience programs grew in popularity from 2000 to 2010, leaders at many institutions began advocating for expanding focus beyond the first year. They reasoned that attrition happens across the entire student journey, driven by a variety of academic, procedural, financial, and personal reasons. In the US, a broad set of reforms known as the “Completion Agenda” sought to organise requirements into standard degree plans and ensure that students could complete them in a timely manner. Institutions that implemented these reforms measured their success via improved graduation rates and degree output.
By the mid-2010s, the completion agenda gave rise to the “Guided Pathways” movement. As the name implies, guided pathways endeavoured to set students on structured pathways matched to their individual education goals. By constructively limiting choice, guided pathways could reduce complexity and keep students on track, improving their odds of completion. Success was measured by shortening time to degree and reducing excess credits.
Lineage C: Next-generation advising
First-year experience programs precipitated the build-out of professional advising staff. As they worked closely with students, advisors could see that attrition was often driven by academic, financial, and personal setbacks, not simply a lack of engagement. In response, they began investing in early alert systems that empowered them to reach out directly to struggling students rather than passively waiting and hoping that they would find help on their own. Success leaders began to rely on metrics like support utilisation and issue resolution as leading indicators of retention.
From 2010 onward, advising offices increasingly became a catch-all for addressing a wide range of student needs, and attrition became understood as a failure to meet these needs. This gave rise to more holistic advising practices and the use of technology to stay in close contact with caseloads, providing support not dissimilar from social work. Success started being tracked via advising engagement and the persistence rates of specific cohorts of students.
Lineage D: Attainment gaps
As early as the 1980s, student success leaders were documenting equity and attainment gaps in higher education access and completion. Many institutions responded by standing up underrepresented minority programs, as they were known at the time, to provide support and community tailored for a student’s identity and background.
By the mid-2010s, affinity programs had morphed into a broader push for promoting diversity and inclusion. Leaders understood that as their student bodies became increasingly diverse, scattered programming was not enough to level the uphill climb that those students experienced. Reformers concentrated their efforts on structures and policies known to have a disproportionate impact on students from underrepresented and lower-income backgrounds. The global antiracism and EDI moment that emerged in 2020 has amplified attention in this space.
The EDI lineage stands out from the others in that it hasn’t necessarily introduced new success metrics into the conversation. Rather, this school of thought asks leaders to disaggregate the metrics advanced by other lineages to understand how success varies across racial, gender, income, and generational lines and identify opportunities for improvement.
Speculating on emerging lineages
Evolution never stops; in fact, it usually accelerates when the underlying environment is in flux and species need to change to keep up. The ripple effects of the pandemic on higher education are already fostering new lineages of thought around student success, adding to what is already a complex ecosystem of ideas. There are four emerging lineages I see as worth monitoring:
Student mental health
The post-pandemic student mental health crisis merely accelerates a trend we have observed for a decade. Soon, measuring student success could include metrics related to mental and emotional well-being – as seen in the inclusion of a question about mental wellbeing services in this year’s NSS.
Developmental education
Primary and secondary learning also took a big hit during the pandemic, and we will be processing the after-effects for years. Indeed, we are already seeing an uptick in students matriculating into university with less preparation than we would have expected just a few years ago. More students will need to engage with developmental education, and the pass rates could become important early indicators of success.
Workforce development
Higher education faces sharp criticisms of its workforce relevance at the exact same time as employers have become more desperate for trained workers. In this environment, institutions that can educate and place workers into highly skilled jobs—and track this success—will be able to use these metrics to tell a different kind of student success story.
Lifelong up-skilling
Workers need to be continuously up-skilled in a fast-changing knowledge economy. Providers that create lifelong affinity will often be the first place their alumni turn to when they need more education to take the next step in their careers. For these institutions, success could become measured by metrics tracking a student’s willingness to ‘renew their contract’ and continue their education with their alma mater.
Defining your strategy and metrics
Needless to say, the quantification of student success has become very complex. Most of this complexity has been introduced in just the last ten years, with more change likely to come, leaving many strategists struggling to set a course for their institutions.
Student success doesn’t need to mean the same thing to all institutions, and it probably shouldn’t. Your strategy should be tailored to the specific needs of your institution’s students, finances, and mission. That necessarily means that you will need to emphasise some of these schools of thought over others as you develop a strategy tailored to your own circumstances and students.
Whatever direction you take, you must define and track the metrics that matter for your chosen strategy. Picking the metrics is the easy part. The bulk of the challenge comes from assembling the underlying data from multiple sources, designing dashboards to easily parse and review these metrics, and developing processes for regular review and revision to strategy.
Our students are changing. Institutions that work hard now to understand these changes and seek areas for improvement will find themselves with a leg in an ever-changing sector.
